1. Binary Forecasts and Observations
Yes/ No; True/False; 1/0
Form the basis for many verification techniques
Used in many applications of weather data
Will the temperature in Phoenix exceed 90 degrees tomorrow?
While quite simple, still commonly used.

2. 2 x 2 Contingency Table
Columns = obs
Rows = Forecast

3. Finley Tornado Data (1884)

4. A success?
Percent Correct = ( )/2803 = !!!!

5. Maybe not. Percent Correct = (0+2752)/2803 = 0.982 – better!!
Critisism from Pierce and Gilbert. (1884)
Percent Correct = (0+2752)/2803 = – better!!

6. Alternative Statistics
Mention – the never forecast has a POD = 0, FAR = 0, CSI = 1
Single statistics can be misleading.
Threat Score = 28 / ( ) = 0.228
Probability of Detection = 28 / ( ) = 0.549
False Alarm Rate= 72/( ) = 0.720

7. Attributes of forecast quality
Accuracy
Bias
Reliability
Resolution
Discrimination
Sharpness

8. Value
If Costs can be associated with false positives and false negative forecasts, one can determine if a forecast has value.
The value of a forecast varies by user.

9. Skill Scores Single value to summarize performance.
Reference forecast - best naive guess; persistence, climatology
Proper skill scores – reflect forecastor true intent.
A perfect forecast implies that the object can be perfectly observed

10. Generic Skill Score
Positively oriented – Positive is good

11. Exercise 2 (ex2.r) Load verification library verify function
frcst.type; obs.type;
Documentation
example()